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library(tidyverse)
library(ggplot2)
library(plotly)
library(naniar)
library(scales)
library(dplyr)
library(glmnet)
library(caret)
library(viridis)
library(reshape2)
options(scipen = 999)#Import data from csv and change datatypes of the columns excluding the columns which are not necessary
raw_data<-read_csv('data/capstone_remit_claims_data.csv')Parsed with column specification:
cols(
.default = col_character(),
date_of_service = [34mcol_datetime(format = "")[39m,
acct_mm = [32mcol_double()[39m,
acct_yyyy = [32mcol_double()[39m,
claim_number = [32mcol_double()[39m,
remit_number = [32mcol_double()[39m,
patient_id = [32mcol_double()[39m,
modality_cost_code = [32mcol_double()[39m,
cost_code = [32mcol_double()[39m,
contract_number = [32mcol_double()[39m,
type_of_care = [32mcol_double()[39m,
remit_total_charged = [32mcol_double()[39m,
remit_total_paid = [32mcol_double()[39m,
remit_allowed_amount = [32mcol_double()[39m,
remits_units = [32mcol_double()[39m
)
See spec(...) for full column specifications.
423 parsing failures.
row col expected actual file
1432 cost_code no trailing characters - 'data/capstone_remit_claims_data.csv'
1433 cost_code no trailing characters - 'data/capstone_remit_claims_data.csv'
1966 remit_allowed_amount a double NULL 'data/capstone_remit_claims_data.csv'
1967 remit_allowed_amount a double NULL 'data/capstone_remit_claims_data.csv'
1968 remit_allowed_amount a double NULL 'data/capstone_remit_claims_data.csv'
.... .................... ...................... ...... .....................................
See problems(...) for more details.
#chnage the columns datatypes to correct data types
df<-raw_data %>%
mutate(patient_id = as.factor(patient_id),
location_id = as.factor(location_id),
#cost_code = as.factor(cost_code),
modality_cost_code = as.character(modality_cost_code),
remit_allowed_amount = as.double(remit_allowed_amount),
contract_number = as.factor(contract_number),
hct_per = as.double(hct_per),
epo_units = as.integer(epo_units),
pcn_payer_code = as.factor(pcn_payer_code),
type_of_care = as.factor(type_of_care),
date_of_service = as.Date(date_of_service)
) %>%
select(-epo_units,-modifier_4,-modifier_5)NAs introduced by coercionNAs introduced by coercion
df<-df %>%
mutate('modality' = case_when(modality_cost_code %in% c('1220','1230',"1121") ~ "PD",
modality_cost_code %in% c('1110',"1210","1310","1510") ~ "HEMO",
modality_cost_code %in% c('1330',"1320") ~ "HOME",
TRUE ~ "OTHERS"))
df<-df %>% replace_with_na (replace = list(diagnosis_code = 'NULL', urr_modifier = 'NULL',modifier_1 = 'NULL',modifier_2='NULL',modifier_3='NULL'))
df <- df %>%
mutate( 'percentage_payment' = remit_total_paid/remit_total_charged * 100)
#SAVE DATAFRAME
saveRDS(df,'clean_df_for_EDA.RDS')
#payors
payors<-as.data.frame(unique(df$pcn_payer_code))
colnames(payors)<-c("payors")
saveRDS(payors,'DCI_capstone/data/payors.rds')
PREPARE A DATAFRAME WHICH CAN BE USED FOR ALGORITHMS
#delete unnecessary columns
algo_df<-df %>%
select (-date_of_service,-acct_mm,-acct_yyyy,-claim_number,-remit_number,-location_id,-patient_id,-contract_number,-urr_modifier,-description,-percentage_payment)
#dummize categorical columns
dummy_cols <- c("modality_cost_code","diagnosis_code","type_of_care","revenue_code", "modifier_1","modifier_2","modifier_3","revenue_code","hcpc_code","pcn_payer_code")
algo_df<-fastDummies::dummy_columns(algo_df,select_columns = c(dummy_cols),ignore_na = TRUE)
#replace na's to 0's
algo_df[is.na(algo_df)]<- 0
algo_df_final<-algo_df %>%
mutate( 'percentage_payment' = remit_total_paid/remit_total_charged * 100) %>%
select (-modality_cost_code,-cost_code,-diagnosis_code,-type_of_care,-revenue_code,-hcpc_code,-modifier_1,-modifier_2,-modifier_3,-pcn_payer_code,-remit_total_charged,-remit_total_paid,-remits_units,-remit_allowed_amount,-modality)
#SAVE DATAFRAME
saveRDS(algo_df_final,'clean_df_for_algorithm.RDS')str(algo_df_final)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 11754 obs. of 125 variables:
$ hct_per : num 33.3 0 33.3 0 33.3 33.3 0 33.3 0 33.3 ...
$ modality_cost_code_1220: int 1 1 0 0 0 0 0 0 0 0 ...
$ modality_cost_code_1330: int 0 0 1 1 1 1 1 1 1 1 ...
$ modality_cost_code_1110: int 0 0 0 0 0 0 0 0 0 0 ...
$ modality_cost_code_1320: int 0 0 0 0 0 0 0 0 0 0 ...
$ modality_cost_code_1310: int 0 0 0 0 0 0 0 0 0 0 ...
$ modality_cost_code_1510: int 0 0 0 0 0 0 0 0 0 0 ...
$ modality_cost_code_1210: int 0 0 0 0 0 0 0 0 0 0 ...
$ modality_cost_code_1230: int 0 0 0 0 0 0 0 0 0 0 ...
$ modality_cost_code_1121: int 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_Z23 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_I50.9 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_J06.9 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_L03.129 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_T88.8XXA: num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_E87.70 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_R68.83 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_I50.32 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_N25.81 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_D50.9 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_G63 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_J91.8 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_E11.621 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_E87.5 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_L03.116 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_D63.1 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_M61.48 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_L03.91 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_Z11.1 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_E83.39 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_N39.0 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_L97.514 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_I50.20 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_B96.89 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_M86.079 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_R11.2 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_Z86.31 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_Z89.519 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_I38 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_I96 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_B95.62 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_G47.33 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_I10 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_I48.91 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_N18.6 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_T82.7XXA: num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_R50.9 : num 0 0 0 0 0 0 0 0 0 0 ...
$ diagnosis_code_I33.0 : num 0 0 0 0 0 0 0 0 0 0 ...
$ type_of_care_71 : int 0 0 0 0 0 0 0 0 0 0 ...
$ type_of_care_73 : int 1 1 0 0 0 0 0 0 0 0 ...
$ type_of_care_74 : int 0 0 1 1 1 1 1 1 1 1 ...
$ type_of_care_76 : int 0 0 0 0 0 0 0 0 0 0 ...
$ type_of_care_84 : int 0 0 0 0 0 0 0 0 0 0 ...
$ type_of_care_87 : int 0 0 0 0 0 0 0 0 0 0 ...
$ revenue_code_0841 : int 1 1 0 0 0 0 0 0 0 0 ...
$ revenue_code_0851 : int 0 0 1 1 1 1 1 1 1 1 ...
$ revenue_code_0821 : int 0 0 0 0 0 0 0 0 0 0 ...
$ revenue_code_0636 : int 0 0 0 0 0 0 0 0 0 0 ...
$ revenue_code_0771 : int 0 0 0 0 0 0 0 0 0 0 ...
$ revenue_code_0270 : int 0 0 0 0 0 0 0 0 0 0 ...
$ revenue_code_0831 : int 0 0 0 0 0 0 0 0 0 0 ...
$ revenue_code_0306 : int 0 0 0 0 0 0 0 0 0 0 ...
$ revenue_code_0300 : int 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_G5 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_G3 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_G1 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_G4 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_AY : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_G6 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_G2 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_JA : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_1_V6 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_2_V6 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_2_V5 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_2_V7 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_2_KX : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_2_JE : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_3_V7 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_3_V6 : num 0 0 0 0 0 0 0 0 0 0 ...
$ modifier_3_KX : num 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_90993 : int 1 1 0 0 0 0 0 0 0 0 ...
$ hcpc_code_90945 : int 0 0 1 1 1 1 1 1 1 1 ...
$ hcpc_code_90999 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_90686 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_90747 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_G0010 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_G0008 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_A4913 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_J0696 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_J3370 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_G0009 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_90732 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_90670 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_J0713 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_A4657 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_J0690 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_J1644 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_J1270 : int 0 0 0 0 0 0 0 0 0 0 ...
$ hcpc_code_J2916 : int 0 0 0 0 0 0 0 0 0 0 ...
[list output truncated]df#Final checks
sum(is.na(algo_df_final))[1] 0SUMMARY ANALYSIS
#Charged
summary(df$remit_total_charged) Min. 1st Qu. Median Mean 3rd Qu. Max.
19.7 870.0 1910.0 1444.3 2010.0 2643.0 #Paid
summary(df$remit_total_paid) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.05 86.61 168.36 154.49 208.80 729.32 charges_dis_plt<-df %>%
ggplot(aes(x=remit_total_charged, y = ..density..)) +
geom_histogram(breaks = seq(0,2800,by=100 ),bins = 10, binwidth = 0.05,
fill = "chartreuse4", alpha = 0.6) +
labs(x="Charges", y = "Counts",title = "Distribution of Charges")+
theme_minimal()+
geom_density(color = "red", alpha=0.4)+
scale_y_continuous(breaks = seq(0,10000,1000))+
scale_x_continuous(breaks = seq(0,3000,500))
ggplotly(charges_dis_plt )
NApayment_dis_plt<-df %>%
ggplot(aes(x=remit_total_paid,y=..density..)) +
geom_histogram(breaks = seq(0,2800,by=100 ),bins = 10, binwidth = 0.05,
fill = "chartreuse4", alpha = 0.6) +
labs(x="Payments", y = "Counts",title = "Distribution of Payments")+
theme_minimal()+
geom_density(color = "red",alpha = 0.4)+
scale_y_continuous(breaks = seq(0,10000,1000))+
scale_x_continuous(breaks = seq(0,3000,500))
ggplotly(payment_dis_plt )pl<- ggplot(df,aes(x=remit_total_charged,y=remit_total_paid) )+
geom_point(alpha= 0.7)
ggplotly(pl)
dci_data_total<-df %>%
filter(acct_yyyy == 2017) %>%
select('Charges' = remit_total_charged,'Payments'=remit_total_paid)
dci_long <- reshape2::melt(dci_data_total)No id variables; using all as measure variables#saveRDS(dci_long,'dist_plts.rds')
dist_plts<-ggplot(dci_long, aes(value,y = ..density..)) + facet_wrap(~variable, scales = 'free_x') +
geom_density(color = "red",alpha = 0.4)+
labs(title = "Distribution")+
geom_histogram( fill = "chartreuse4", alpha = 0.6)+
# geom_vline(aes(xintercept = mean(value,na.rm=T)),color = "red", linetype = "dashed",size = 1) +
scale_y_continuous(breaks = seq(0,10000,1000))+
scale_x_continuous(breaks = seq(0,3000,500))
dist_plts
df %>%
ggplot(aes(x=remit_total_paid)) + geom_histogram(binwidth = 50)
df %>%
ggplot(aes(x=remit_total_paid,fill = modality)) +
geom_vline(aes(xintercept = mean(remit_total_paid,na.rm=T)),color = "red", linetype = "dashed",size = 1) +
geom_histogram(bins=40,binwidth = 10, center = 0.05,alpha = 0.8)
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
dist_box_modality<-df %>%
filter(acct_yyyy == 2017) %>%
select(modality,remit_total_paid) %>%
ggplot(aes( modality, remit_total_paid,alpha = 0.5)) +
geom_boxplot(aes(fill=modality)) +
theme_classic() +
scale_fill_manual(values = cbp1) +
ggtitle("Distribution of Payments across Modalities") +
labs(x="Modality",y = "Payments") +
theme(legend.position = "none")
ggplotly(dist_box_modality)
NApayorspayorsdf %>%
# filter(pcn_payer_code =='24180402') %>%
#filter(modality == 'PD') %>%
group_by(modality,pcn_payer_code) %>%
summarise(sum_payments= sum(remit_total_paid))
total_payments_bar_plt<-df %>%
# filter(pcn_payer_code =='24180402') %>%
#filter(modality == 'PD') %>%
group_by(modality) %>%
summarise(sum_payments= sum(remit_total_paid)) %>%
ggplot(aes(x = modality, y = sum_payments, fill = modality)) +
geom_bar(stat = "identity", alpha=0.8) +
theme_classic() +
labs(
x = "Modality",
y = "Total Payments",
title = "Total Payments by Modality"
) +
scale_fill_manual(values = cbp1) +
theme(legend.position = "none")
ggplotly(total_payments_bar_plt)END OF SUMMARY ANALYSIS
BEGIN TIME SERIS ANALYSIS
avg_pct_paid_over_time<-df %>%
filter(acct_yyyy == 2018) %>%
mutate('percentage_paid' = remit_total_paid/remit_total_charged*100) %>%
mutate('period' = paste(acct_mm , '/' , acct_yyyy)) %>%
select(acct_mm,acct_yyyy,period,percentage_paid,pcn_payer_code) %>%
group_by(acct_mm,period,pcn_payer_code) %>%
summarise('avg_pct_paid'=mean(percentage_paid))
time_plot<-ggplot(avg_pct_paid_over_time, aes( reorder(period,acct_mm) , avg_pct_paid, group = pcn_payer_code, color = pcn_payer_code)) +
geom_line(size = 1) +
geom_point(size=2, shape=6, aes(fill=factor(pcn_payer_code))) +
theme_minimal()+
labs(color = "Payors", fill = 'Payors')+
ylab('Percentage of Payments ') +
xlab('Month/Year') +
ylim(0, 50)+
ggtitle('Percentage of Payments by Payor over time')+
theme(axis.text.x = element_text(angle = 90,hjust=1))
ggplotly(time_plot) df %>%
filter(acct_yyyy == 2018) %>%
mutate('percentage_paid' = remit_total_paid/remit_total_charged*100) %>%
mutate('period' = paste(acct_mm , '/' , acct_yyyy)) %>%
select(acct_mm,acct_yyyy,period,percentage_paid,pcn_payer_code) %>%
group_by(acct_mm,acct_yyyy,period,pcn_payer_code) %>%
summarise('avg_pct_paid'=mean(percentage_paid)) %>%
arrange(acct_mm,avg_pct_paid) %>%
ggplot(aes( reorder(x = period,acct_mm) ,y = avg_pct_paid,group=1,color=pcn_payer_code,fill=pcn_payer_code)) +
geom_point() +
theme(axis.text.x = element_text(angle = 45,hjust=1))
ANALYSIS BY LOCATION (can be a page)
# payment percentage by location
df %>%
group_by(location_id) %>%
summarise('Num_of_rows'=n(),
'Total_charges' = sum(remit_total_charged),
'Total_payments' = sum(remit_total_paid),
'percentage_payments' = (Total_payments/Total_charges) * 100,
) %>%
arrange(desc(Total_charges))NAALANYSIS BY PCN (PAYORS)
# payment percentage by PCN
charge_payments<- df %>%
group_by(pcn_payer_code) %>%
summarise('Num_of_rows'=n(),
'Total_charges' = sum(remit_total_charged),
'Total_payments' = sum(remit_total_paid),
'percentage_payments' = (Total_payments/Total_charges) * 100,
) %>%
arrange(percentage_payments)charge_payments %>%
mutate('mean_percentage_payments' = sum(Total_payments)/sum(Total_charges)*100) %>%
mutate('dis_from_avg_per_payment' = percentage_payments-mean_percentage_payments )payors_with_good_per<-df %>%
group_by(pcn_payer_code) %>%
summarise('Num_of_rows'=n(),
'Total_charges' = sum(remit_total_charged),
'Total_payments' = sum(remit_total_paid),
'percentage_payments' = (Total_payments/Total_charges) * 100,
) %>%
arrange(desc(Total_charges)) %>%
ggplot(aes(reorder(x=pcn_payer_code,percentage_payments),y=percentage_payments,fill = pcn_payer_code)) +
geom_col(alpha=0.8) +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45,hjust=1))+
labs(x='Payors',y="Percentage of charges paid",title = "Which payors are paying good percentage?")
ggplotly(payors_with_good_per)
NA#Which payer is paying good percentage by modality
df %>% group_by(pcn_payer_code,modality) %>%
summarise('Num_of_rows'=n(),
'Total_charges' = sum(remit_total_charged),
'Total_payments' = sum(remit_total_paid),
'percentage_payments' = (Total_payments/Total_charges) * 100,
) %>%
arrange(desc(Total_charges)) payors_with_good_per<-df %>% group_by(pcn_payer_code,modality) %>%
filter(pcn_payer_code=='02371001') %>%
summarise('Num_of_rows'=n(),
'Total_charges' = sum(remit_total_charged),
'Total_payments' = sum(remit_total_paid),
'percentage_payments' = round((Total_payments/Total_charges) * 100,2)
) %>%
arrange(desc(Total_charges)) %>%
ggplot(aes(reorder(x=modality,percentage_payments),y=percentage_payments,fill = modality)) +
geom_col(alpha=0.8) +
geom_text(aes(label=percentage_payments), vjust=0) +
theme_minimal()+
labs(x='Modality',y="Percentage of charges paid",title = "Percentage of Charges paid by Modality?")+
theme( theme(legend.position = "none"))Error in (function (el, elname) :
Element line must be a element_line object.df NA
NA payors_with_good_per<-df %>% group_by(pcn_payer_code,modality) %>%
filter(pcn_payer_code=='02371001') %>%
summarise('Num_of_rows'=n(),
'Total_charges' = sum(remit_total_charged),
'Total_payments' = sum(remit_total_paid),
'percentage_payments' = round((Total_payments/Total_charges) * 100,2)
) %>%
arrange(desc(Total_charges)) %>%
ggplot(aes(reorder(x=modality,percentage_payments),y=percentage_payments,fill = modality)) +
geom_segment(aes (reorder(x=modality,percentage_payments), xend =modality,y=0,yend = percentage_payments) )+
geom_point(color = "orange",size = 4)+
theme_minimal()+
geom_text(aes(label=percentage_payments)) +
labs(x='Modality',y="Percentage of Charges Paid(%)",title = "Percentage of Charges Paid by Modality?")+
scale_y_continuous(breaks = seq(0,20,1))+
theme(legend.position = "none")
ggplotly(payors_with_good_per)BY LOCAITON, WHICH PAYORS ARE CONTRIBUTING MORE (CAN BE A TAB)
df %>%
group_by(location_id,pcn_payer_code) %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('loss_amt' = total_charges-total_paid) %>%
mutate('payment_percentage' = (total_paid/total_charges)*100) %>%
mutate('loss_percentage' = (loss_amt/total_charges) * 100) %>%
filter(total_paid!=0) %>%
arrange(location_id,desc(payment_percentage))unique(df$pcn_payer_code) [1] 02371001 24180402 06791013 11371008 12320609 21560402 00410417 16181025 00410601 00410825 06771001 00410901 00410235
Levels: 00410235 00410417 00410601 00410825 00410901 02371001 06771001 06791013 11371008 12320609 16181025 21560402 24180402colnames(df) [1] "date_of_service" "acct_mm" "acct_yyyy" "claim_number" "remit_number"
[6] "patient_id" "location_id" "modality_cost_code" "cost_code" "diagnosis_code"
[11] "contract_number" "hct_per" "urr_modifier" "type_of_care" "revenue_code"
[16] "description" "hcpc_code" "remit_total_charged" "remit_total_paid" "remit_allowed_amount"
[21] "remits_units" "modifier_1" "modifier_2" "modifier_3" "pcn_payer_code"
[26] "modality" "percentage_payment" distinct(df,acct_yyyy,pcn_payer_code ) %>%
filter(acct_yyyy==2018) df%>%
group_by(description) %>%
filter (pcn_payer_code =='00410235') %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>%
mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) #filter(total_paid>0)Services by each payor type
services_plt <- df%>%
group_by(description) %>%
filter (pcn_payer_code =='02371001') %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>%
mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>%
filter(total_paid>0) %>%
ggplot(aes(reorder(x= description,total_paid),y = total_paid,fill = description)) +
geom_col()+
coord_flip()+
theme(axis.text.x = element_text(angle = 45,hjust=1)) +
theme_minimal()+
xlab("Services") +
ylab("Payments") +
ggtitle("Payments by Services")+
theme(legend.position = "none")
ggplotly(services_plt)FOR WHAT TYPE OF TREATMENT / MEDICATION ARE WE GETTING PAID LESS / MORE THAN 10 PERCENT (CAN BE A PAGE)
x<- df %>%
group_by('Description'=description) %>%
summarise('total_charges'=sum(remit_total_charged),
'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_paid' = round(total_paid/total_charges * 100,2)) %>%
arrange(desc(percentage_paid) )
saveRDS(x$Description,'data/meds.rds')df %>%
filter(description == 'ARANESP/DARBEPOETIN ALFA-' ) %>%
group_by(description) %>%
summarise('TotalCharges'=sum(remit_total_charged),'TotalPaid' = sum(remit_total_paid)) %>%
pivot_longer(TotalCharges:TotalPaid,names_to = "Type", values_to = "Charges") df %>%
group_by('Description'=description) %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage' = round(total_paid/total_charges * 100,2)) %>%
select (Description,percentage) Medications for which less than 10 percent paid
##top cost codes
meds<-df %>%
filter(description == 'ARANESP/DARBEPOETIN ALFA-' ) %>%
group_by(description) %>%
summarise('TotalCharges'=sum(remit_total_charged),'TotalPaid' = sum(remit_total_paid)) %>%
pivot_longer(TotalCharges:TotalPaid,names_to = "Type", values_to = "Charges")%>%
ggplot(aes(x=reorder(description,Charges),y=Charges,fill = Type) ) +
geom_bar(stat="identity",alpha = 0.8,position = 'dodge') +
theme(axis.text.x = element_text(angle = 45,hjust=1)) +
theme_minimal()+
xlab("Medication") +
ylab("Dollars") +
ggtitle("Total Charges vs Total Paid")
ggplotly(meds)meds%>%
arrange(desc(avg_per_paid))Difference in payments by modality on medications
#only medications paid by cost_code
meds<- df %>%
select(modality_cost_code,cost_code,description,remit_total_charged,remit_total_paid,modality) %>%
filter(cost_code>3000 ) %>%
mutate('percentage_paid' = round(remit_total_paid/remit_total_charged * 100,2)) %>%
group_by(modality, description) %>%
summarize('avg_per_paid'=mean(percentage_paid))
meds<- meds%>%
filter(avg_per_paid <54)
options(repr.plot.width = 3, repr.plot.height = 3)
x<- ggplot(meds,aes(reorder(x=description,avg_per_paid), y = avg_per_paid,fill = modality )) +
geom_col(stat="identity",position = 'dodge' ) +
ggtitle("Medication Payments by Modality") +
labs(x="Medications", y = 'Average of percentage Payments') +
theme(axis.text.x = element_text(angle = 45,hjust=1)) Ignoring unknown parameters: stat
ggplotly(x)df %>%
select(modality_cost_code,cost_code,description,remit_total_charged,remit_total_paid) %>%
filter(cost_code>3000 & cost_code<5000) %>%
mutate('percentage_paid' = round(remit_total_paid/remit_total_charged * 100,2)) %>%
group_by(modality_cost_code,cost_code,description) %>%
summarize('avg_per_paid'=mean(percentage_paid)) %>%
arrange(cost_code,modality_cost_code) List of HCPC codes for drugs
distinct(df,cost_code,description,modality_cost_codes) Trying to compute distinct() for variables not found in the data:
- `modality_cost_codes`
This is an error, but only a warning is raised for compatibility reasons.
The following variables will be used:
- cost_code
- descriptionNA
NAANALYSIS ON TREATMENTS
treatment_summaries %>%
ggplot(aes(reorder(x=description,total_paid),y=total_paid,fill = description))+
geom_bar(stat="identity")+
coord_flip()+
theme_bw()+
theme(axis.text.x = element_text(angle = 45,hjust=1))+
# geom_text(aes(label=total_paid), vjust=0) +
xlab("Treatment Types") +
ylab("Number of Treatments") +
ggtitle("Payments by Treatments") +
theme(legend.position = "none")
NAtreatment_summaries %>%
ggplot(aes(reorder(x=description,percentage_paid),y=percentage_paid,fill = description))+
geom_bar(stat="identity")+
geom_text(aes(label=percentage_paid), vjust=0) +
#coord_flip()+
theme(axis.text.x = element_text(angle = 45,hjust=1))+
xlab("Treatments") +
ylab("Percentage of Charges paid back") +
ggtitle("For which Treatments Good Percentage of Charge is Paid Back")+
theme(legend.position = "none")
ggdotchart(treatment_summaries, x = "description", y = "percentage_paid",
color = "description", # Color by groups
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7"),
# Custom color palette
sorting = "ascending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
ggtheme = theme_pubr(),
size = 8
) +
geom_text(aes(label=percentage_paid), vjust=0,size=3) +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 360,hjust=1))+
coord_flip() +
labs(title = "Percentage Paid Back by Treatment Types",x="Treatment Types", y = "Percentage %")
PAYMENTS STRUCTURE OF PAYORS
df %>%
group_by(pcn_payer_code) %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>%
mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2))df %>%
group_by(pcn_payer_code) %>%
filter (pcn_payer_code !='02371001') %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>%
mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>%
arrange(desc(per_payment_overall)) %>%
ggplot(aes(reorder(x=pcn_payer_code,per_payment_overall),y=per_payment_overall,fill = reorder(x=pcn_payer_code,per_payment_overall))) +
geom_col(stat="identity") +
theme(axis.text.x = element_text(angle = 90,hjust=1)) +
theme(legend.position = "none")+
ggtitle("Other Payors payment percentage")+
labs(x= "Payors",y="Percentage of Payments")Ignoring unknown parameters: stat
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test <- compare_actuals_to_pred %>%
# filter(!!as.symbol(input$algo_payors) == 1 ) %>%
# filter(!!as.symbol(input$algo_modality) == 1)%>%
filter(pcn_payer_code_02371001 == 1 & hcpc_code_90999==1 ) %>%
select(actual,predicted,percentage_difference ) %>%
summarise('Actual' = mean(actual),'Predicted' = mean(predicted)) %>%
pivot_longer(Actual:Predicted,names_to = "Predictions", values_to = "percentage_predicted")
get_percentage_value<-test %>%
filter(Predictions == "Predicted") %>%
select(percentage_predicted)
#as.vector(get_percentage_value)
get_percentage_value * 100NAPayors_bar_plt<-df %>%
group_by(pcn_payer_code) %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_of_payments' = round(total_paid/sum(total_paid)*100,2)) %>%
ggplot(aes(reorder(x=pcn_payer_code,percentage_of_payments),y=percentage_of_payments,fill = pcn_payer_code,text=percentage_of_payments))+
theme(axis.text.x = element_text(angle = 90,hjust=1)) +
geom_bar( stat = "identity")+
theme(legend.position = "none") +
labs(x="Payors",y="Percentage of Total Payments",Title = "Percentage of Total Payments For Each Payor ")
ggplotly(Payors_bar_plt,tooltip = "text")PERCENTAGE OF PAYMENTS BY PAYOR
bp<-df %>%
group_by(pcn_payer_code) %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_of_payments' = round(total_paid/sum(total_paid)*100,2)) %>%
ggplot(aes(x="",y=percentage_of_payments,fill = pcn_payer_code))+
geom_bar(width = 1, stat = "identity")
bp
pie_chart <- bp + coord_polar("y",start = 0) +
labs(title = "Majority of Payments coming from Payor: 02371010") +
theme_void()
pie_chart
#92.58
TAKE OUT THE MAJOR PAYOR AND SEE HOW OTHERS ARE DOING
bp<-df %>%
group_by(pcn_payer_code) %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_of_payments' = round(total_paid/sum(total_paid)*100,2)) %>%
mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>%
filter(pcn_payer_code !='02371001') %>%
ggplot(aes(x="",y=percentage_of_payments,fill = pcn_payer_code))+
geom_bar(width = 1, stat = "identity")
bp
pie_chart <- bp + coord_polar("y",start = 0) +
labs(title = "Payments By Other Payors") +
theme_void()
geom_text(aes(y = value/3 + c(0, cumsum(value)[-length(value)]),
label = percent(value/100)), size=5)mapping: y = ~value/3 + c(0, cumsum(value)[-length(value)]), label = ~percent(value/100)
geom_text: parse = FALSE, check_overlap = FALSE, na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity pie_chart
ggplot(df) +
geom_bar(aes(x=pcn_payer_code, y=remit_total_paid, color=pcn_payer_code),stat="identity") + geom_smooth(aes(x=pcn_payer_code, y=remit_total_paid))+
theme(axis.text.x = element_text(angle = 90,hjust=1)) +
theme(legend.position = "none")
PAYMENTS BY TREATMENTS
ts<-treatment_summaries %>%
ggplot(aes(x=reorder(description,percentage_paid),y=percentage_paid,fill = description))+
geom_bar(stat = "identity")+
theme(axis.text.x = element_text(angle = 45,hjust=1)) +
theme(legend.position = "none")
ts
NA
NAPAYMENTS BY NON-TREATMENT SERVICES COST CODES like meds supplies etc
df %>%
filter(cost_code >'1510') %>%
group_by(cost_code ) %>%
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>%
mutate('percentage_paid' = total_paid/total_charges * 100) %>%
filter(percentage_paid>0) %>%
mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>%
arrange(desc(per_payment_overall)) %>%
ggplot(aes(x= factor(cost_code),y=per_payment_overall,fill = factor(cost_code))) +
geom_col(stat_count = "identity")+
theme(axis.text.x = element_text(angle = 90,hjust=2)) +
theme(legend.position = "none")Ignoring unknown parameters: stat_count
distinct(df,cost_code,description)%>%
filter(cost_code %in% c('3605','3621','7060','3613'))df %>%
select(date_of_service,patient_id,cost_code,remit_total_paid,pcn_payer_code) %>%
ggplot(aes(x=as.factor(cost_code),y=remit_total_paid)) +geom_point()+
theme(axis.text.x = element_text(angle = 90,hjust=1)) +
labs(x="Cost Codes",y="Total payments",title = "Total Payments by Cost Codes")
IS THERE ANY RELATION BETWEEN PAYMENT AND HCT_PER? NO
ANALYSIS USING MODIFIERS
G1 - Most recent URR of less than 60% G2 - Most recent URR of 60% to 64.9% G3 - Most recent URR of 65% to 69.9% G4 - Most recent URR of 70% to 74.9% G5 - Most recent URR of 75% or greater G6 - ESRD patient for whom less than seven dialysis sessions have been provided in a month.
DIAGNOSIS CODE ANALYSIS
dplyr::count(df,diagnosis_code,sort = TRUE)N25.81 - Secondary hyperparathyroidism of renal origin Z23 is a billable ICD code used to specify a diagnosis of encounter for immunization.
diagnosis<-df %>% group_by(diagnosis_code) %>% summarise(n=n(),total_charged = sum(remit_total_charged), total_paid = sum(remit_total_paid)) %>% arrange(desc(n))
diagnosisNA
diagnosis %>%
filter(!is.na(diagnosis_code)) %>%
ggplot(aes(x=diagnosis_code,y=n,fill=diagnosis_code)) +
geom_col() +
theme(axis.text.x = element_text(angle = 90,hjust=1)) 
BEGIN MAKING PERCENTAGE OF PAYMENTS PREDICTION USING REGRESSION ALGORITHMS.
- ORDINARY LEAST SQUARES MODEL
#First, split the data in algo_df_final dataset with 75 percentile
set.seed(237)
index = createDataPartition(algo_df_final$hct_per,p=0.75,list = FALSE)
#save the 75% of data in trainSet and 25% in test set.
trainSet <- algo_df_final[index,]
testSet <- algo_df_final[-index,]#OLS
#train the model
#ols_ds <- train(percentage_payment~.,data = trainSet,method = "lm", metric ="RMSE",
# trControl = trainControl(method = "none"))
ols_ds <- train(percentage_payment~.,data = trainSet,method = "lm")prediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleadingprediction from a rank-deficient fit may be misleading#training set performance
train_pred <- predict(ols_ds, newdata = trainSet)prediction from a rank-deficient fit may be misleadingMAE(pred = train_pred , obs = trainSet$percentage_payment)#1.555435[1] 1.555435print(ols_ds)Linear Regression
8818 samples
124 predictor
No pre-processing
Resampling: Bootstrapped (25 reps)
Summary of sample sizes: 8818, 8818, 8818, 8818, 8818, 8818, ...
Resampling results:
RMSE Rsquared MAE
3.212291 0.9259262 1.639011
Tuning parameter 'intercept' was held constant at a value of TRUE#test set
test_pred <- predict (ols_ds, newdata = testSet)prediction from a rank-deficient fit may be misleadingMAE(pred = test_pred, obs = testSet$percentage_payment) [1] 1.652259print(test_pred) 1 2 3 4 5 6 7 8 9 10
11.3154148 11.3154148 10.5213993 9.1944347 9.8954573 9.8954573 9.1944347 9.1944347 9.1944347 9.1944347
11 12 13 14 15 16 17 18 19 20
9.8954573 9.1944347 26.8666461 66.1131325 12.6951999 12.6951999 12.6951999 12.6951999 12.6951999 12.6951999
21 22 23 24 25 26 27 28 29 30
10.1478820 10.7230827 10.7230827 10.7230827 8.9607568 9.4168000 9.4168000 9.1300550 9.1300550 9.1300550
31 32 33 34 35 36 37 38 39 40
9.1300550 9.3446538 9.3446538 9.2078918 9.2078918 9.2078918 9.4725636 9.4725636 9.4725636 9.4725636
41 42 43 44 45 46 47 48 49 50
9.6816913 9.6816913 9.6816913 9.6816913 9.6816913 1.4056111 0.7332016 10.0671197 10.0671197 10.0671197
51 52 53 54 55 56 57 58 59 60
10.0671197 10.0671197 10.0671197 10.0671197 10.0671197 10.0671197 10.0671197 10.0671197 10.0671197 10.0671197
61 62 63 64 65 66 67 68 69 70
12.6308203 12.6308203 12.6308203 12.6308203 12.6308203 12.6308203 10.1550353 10.1550353 10.4999395 10.4999395
71 72 73 74 75 76 77 78 79 80
10.4999395 10.4999395 10.4999395 10.4999395 10.4999395 10.4999395 10.4999395 10.4999395 10.4999395 10.4999395
81 82 83 84 85 86 87 88 89 90
10.4999395 9.0370622 1.2590227 1.2590227 9.0370622 9.0370622 9.9107462 9.9107462 9.7103045 9.7103045
91 92 93 94 95 96 97 98 99 100
9.7103045 9.7103045 10.9204375 9.9976628 10.9204375 10.9204375 10.9204375 9.9976628 10.9204375 9.9976628
101 102 103 104 105 106 107 108 109 110
10.9204375 9.9976628 9.9976628 10.9204375 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628
111 112 113 114 115 116 117 118 119 120
9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628
121 122 123 124 125 126 127 128 129 130
9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628
131 132 133 134 135 136 137 138 139 140
9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628 9.9976628
141 142 143 144 145 146 147 148 149 150
9.9177500 9.9177500 10.3835519 10.1119104 10.3835519 9.9547431 9.9547431 10.9204375 9.9547431 9.9547431
151 152 153 154 155 156 157 158 159 160
9.9547431 9.9547431 10.9204375 10.9204375 10.9204375 9.9547431 10.9204375 9.9547431 9.9547431 10.9204375
161 162 163 164 165 166 167 168 169 170
9.9547431 10.9204375 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431
171 172 173 174 175 176 177 178 179 180
9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431
181 182 183 184 185 186 187 188 189 190
9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431
191 192 193 194 195 196 197 198 199 200
9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 9.9547431 10.3338676 10.4713263 10.4713263
201 202 203 204 205 206 207 208 209 210
10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 10.4713263
211 212 213 214 215 216 217 218 219 220
10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 9.5297900 9.5297900 8.8367701
221 222 223 224 225 226 227 228 229 230
10.0742730 10.0742730 10.0742730 10.0742730 10.0742730 10.0742730 9.7603776 9.4766637 9.9327068 10.6273748
231 232 233 234 235 236 237 238 239 240
9.4766637 10.1713317 10.1713317 9.4766637 10.1713317 9.4766637 9.7209424 9.4766637 10.0119694 10.9204375
241 242 243 244 245 246 247 248 249 250
10.0119694 10.9204375 10.0119694 10.9204375 10.0119694 10.9204375 10.0119694 10.9204375 10.9204375 10.0119694
251 252 253 254 255 256 257 258 259 260
10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694
261 262 263 264 265 266 267 268 269 270
10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694
271 272 273 274 275 276 277 278 279 280
10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694
281 282 283 284 285 286 287 288 289 290
10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694 10.0119694
291 292 293 294 295 296 297 298 299 300
10.0119694 10.1815724 10.1815724 10.1815724 10.0835023 10.0835023 10.0835023 10.0835023 10.0835023 10.2815348
301 302 303 304 305 306 307 308 309 310
10.2815348 10.2815348 10.2815348 10.2815348 10.2815348 10.6358520 10.6358520 10.6358520 10.6358520 10.6358520
311 312 313 314 315 316 317 318 319 320
10.6358520 10.6358520 9.1872814 9.1872814 9.1872814 9.2221984 9.2221984 9.7246111 10.1679315 10.1679315
321 322 323 324 325 326 327 328 329 330
10.1679315 10.4069467 11.3154148 10.4069467 11.3154148 10.4069467 11.3154148 10.4069467 10.4069467 11.3154148
331 332 333 334 335 336 337 338 339 340
11.3154148 11.3154148 10.4069467 10.4069467 10.4069467 11.3154148 11.3154148 10.4069467 11.3154148 10.4069467
341 342 343 344 345 346 347 348 349 350
10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467
351 352 353 354 355 356 357 358 359 360
10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467 10.4069467
361 362 363 364 365 366 367 368 369 370
10.4069467 10.4069467 10.0814263 10.0814263 10.6644652 10.6644652 10.6644652 4.4602593 10.6644652 10.6644652
371 372 373 374 375 376 377 378 379 380
10.6644652 14.8870121 14.4309690 12.1163728 12.1163728 9.1658215 9.1658215 9.1658215 9.2221984 10.0742730
381 382 383 384 385 386 387 388 389 390
10.0742730 10.2726090 10.2726090 10.2726090 10.2726090 10.1822381 20.9218467 10.1822381 10.1822381 10.5285526
391 392 393 394 395 396 397 398 399 400
10.5285526 10.5285526 10.5285526 10.5285526 10.5285526 10.5285526 10.5285526 10.0313532 10.0313532 10.0313532
401 402 403 404 405 406 407 408 409 410
10.0313532 10.0385065 10.0385065 10.0385065 10.0385065 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461
411 412 413 414 415 416 417 418 419 420
10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461
421 422 423 424 425 426 427 428 429 430
10.5142461 10.5142461 10.5142461 10.4641730 10.4641730 10.4641730 10.4641730 10.4641730 10.4641730 11.6102566
431 432 433 434 435 436 437 438 439 440
11.6102566 11.6102566 11.6102566 9.1864320 9.1864320 9.1864320 10.0885796 10.0885796 9.7460710 9.7460710
441 442 443 444 445 446 447 448 449 450
9.7460710 9.7317644 9.7317644 9.7317644 9.7317644 10.0320190 10.0320190 10.0320190 10.0320190 9.9812802
451 452 453 454 455 456 457 458 459 460
9.9812802 9.9812802 20.9218467 10.8468285 9.9812802 9.9812802 10.8468285 9.9812802 10.8468285 10.8468285
461 462 463 464 465 466 467 468 469 470
9.9812802 9.9812802 10.4641730 10.4641730 10.4641730 10.4641730 10.0313532 60.1683331 10.0313532 10.0313532
471 472 473 474 475 476 477 478 479 480
10.0313532 10.0313532 10.0313532 10.0313532 10.0313532 10.0313532 10.0313532 11.1846813 10.6000856 10.6000856
481 482 483 484 485 486 487 488 489 490
10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856
491 492 493 494 495 496 497 498 499 500
10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856 10.6000856
501 502 503 504 505 506 507 508 509 510
10.6000856 10.6000856 9.2373544 9.2373544 9.2373544 9.2150451 9.2150451 9.2150451 9.7818374 10.0385065
511 512 513 514 515 516 517 518 519 520
10.0385065 10.0385065 10.0385065 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451
521 522 523 524 525 526 527 528 529 530
0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 18.6466643 0.5344451 0.5344451
531 532 533 534 535 536 537 538 539 540
0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451
541 542 543 544 545 546 547 548 549 550
0.5344451 0.5344451 0.5344451 0.5344451 0.2577418 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451
551 552 553 554 555 556 557 558 559 560
0.5344451 0.5344451 0.5344451 0.5344451 0.2577418 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451
561 562 563 564 565 566 567 568 569 570
0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.2577418 0.5344451
571 572 573 574 575 576 577 578 579 580
0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451
581 582 583 584 585 586 587 588 589 590
0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451
591 592 593 594 595 596 597 598 599 600
0.5344451 0.2577418 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451
601 602 603 604 605 606 607 608 609 610
0.5344451 18.6466643 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 0.5344451 18.6466643
611 612 613 614 615 616 617 618 619 620
0.2577418 10.0027401 10.0027401 10.0027401 10.5213993 10.5213993 10.5213993 10.5213993 10.5213993 10.5213993
621 622 623 624 625 626 627 628 629 630
10.4371347 10.4371347 9.8531867 9.8531867 9.8531867 9.8531867 9.8531867 9.8531867 9.6816913 9.6816913
631 632 633 634 635 636 637 638 639 640
9.6816913 60.1683331 10.2244921 10.2244921 10.2244921 9.9177500 9.9177500 9.9177500 9.9177500 9.9177500
641 642 643 644 645 646 647 648 649 650
9.9177500 9.8247572 20.9218467 60.1683331 9.8247572 9.8247572 9.8247572 9.1721254 9.1721254 9.7889907
651 652 653 654 655 656 657 658 659 660
9.7889907 9.7889907 9.9240538 9.9240538 9.9240538 9.7031512 9.7031512 9.7031512 9.7031512 9.7532243
661 662 663 664 665 666 667 668 669 670
9.7532243 9.7532243 9.7532243 9.7532243 10.5442504 10.5442504 7.9468085 98.5740142 9.2150451 9.2150451
671 672 673 674 675 676 677 678 679 680
9.8954573 9.8954573 9.8954573 9.8954573 9.8954573 9.8954573 9.1157485 9.1157485 9.1157485 9.1157485
681 682 683 684 685 686 687 688 689 690
9.1157485 9.1157485 9.7389177 9.7389177 10.2408748 10.2408748 10.2408748 10.2408748 10.4713263 10.4713263
691 692 693 694 695 696 697 698 699 700
10.4713263 10.4713263 10.4713263 10.4713263 10.4713263 9.8986686 9.8986686 9.8986686 9.8986686 10.4355598
701 702 703 704 705 706 707 708 709 710
10.4355598 10.4355598 10.4355598 11.3154148 10.4355598 10.4355598 11.3154148 11.3154148 11.3154148 10.4355598
711 712 713 714 715 716 717 718 719 720
11.3154148 11.3154148 10.4355598 10.4355598 10.4355598 10.4355598 10.4355598 80.4289910 80.4289910 80.4289910
721 722 723 724 725 726 727 728 729 730
80.4289910 80.4289910 80.4289910 80.4289910 10.2654557 10.2654557 10.2654557 10.0885796 10.0885796 10.0885796
731 732 733 734 735 736 737 738 739 740
10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461 10.5142461
741 742 743 744 745 746 747 748 749 750
9.6673848 9.6673848 9.6673848 9.6673848 9.1864320 9.1864320 9.7603776 9.7603776 9.7603776 9.9741269
751 752 753 754 755 756 757 758 759 760
9.9741269 9.8319105 9.8319105 9.8319105 9.8319105 9.8319105 9.8319105 13.8052790 13.8052790 13.8052790
761 762 763 764 765 766 767 768 769 770
13.3602287 13.3602287 13.3602287 13.3602287 13.3602287 13.3602287 12.8096526 12.8096526 66.1131325 14.5264512
771 772 773 774 775 776 777 778 779 780
10.2623346 10.2623346 11.1059950 13.4677555 13.4677555 12.7452730 13.4677555 12.7452730 12.7452730 12.7452730
781 782 783 784 785 786 787 788 789 790
10.7302360 9.3824384 10.7302360 1.1177784 60.6697236 9.3824384 9.3824384 10.7302360 9.3824384 10.7302360
791 792 793 794 795 796 797 798 799 800
10.7302360 10.8468285 10.8468285 10.5500125 10.5500125 10.5500125 60.1683331 10.6944696 10.6944696 10.6944696
801 802 803 804 805 806 807 808 809 810
10.0241999 9.6959979 9.6959979 9.6959979 98.5740142 98.5740142 98.5740142 10.3459144 10.0742730 9.2651182
811 812 813 814 815 816 817 818 819 820
9.2651182 9.2651182 10.1407287 10.1407287 10.1407287 10.1407287 10.1407287 10.1407287 10.1407287 10.1407287
821 822 823 824 825 826 827 828 829 830
9.7532243 9.7532243 9.7532243 9.7532243 10.4141000 20.2034995 10.4141000 10.4141000 10.4141000 10.4141000
831 832 833 834 835 836 837 838 839 840
10.4141000 10.0170466 10.0170466 10.0170466 10.0170466 10.0170466 9.8319105 9.8319105 9.8319105 9.8319105
841 842 843 844 845 846 847 848 849 850
13.7623592 13.7623592 13.7623592 13.7623592 14.0040250 14.0040250 14.0040250 14.0040250 14.0040250 14.0040250
851 852 853 854 855 856 857 858 859 860
11.3311126 11.3311126 11.3311126 11.3311126 10.5142461 10.5142461 10.5142461 10.9416923 10.9416923 10.9416923
861 862 863 864 865 866 867 868 869 870
10.9416923 10.9416923 10.9416923 10.9416923 10.9416923 9.1515149 9.1515149 9.1515149 9.1515149 10.4999395
871 872 873 874 875 876 877 878 879 880
10.4999395 10.4999395 10.4999395 10.4999395 10.4999395 10.4999395 10.4999395 10.1393184 10.1393184 10.1393184
881 882 883 884 885 886 887 888 889 890
10.1393184 10.0313532 10.0313532 10.0313532 10.0313532 9.7317644 11.2070827 20.9218467 11.2070827 10.3369886
891 892 893 894 895 896 897 898 899 900
9.2293517 9.2293517 9.2293517 9.2293517 9.5815453 9.5815453 9.5815453 10.2583024 10.2583024 10.2583024
901 902 903 904 905 906 907 908 909 910
10.2583024 9.7460710 9.7460710 9.7460710 9.7460710 11.2419076 10.2337215 10.2337215 10.2337215 10.2337215
911 912 913 914 915 916 917 918 919 920
10.2337215 10.2337215 9.8390638 10.5285526 10.5285526 10.5285526 10.5285526 10.5285526 10.5285526 10.5285526
921 922 923 924 925 926 927 928 929 930
10.5285526 13.1310656 10.7216915 10.7216915 10.7216915 10.7216915 10.7216915 10.4102940 10.4102940 10.4102940
931 932 933 934 935 936 937 938 939 940
20.9218467 10.4102940 10.4102940 100.0000000 100.0000000 10.0866234 10.0866234 10.0866234 10.0866234 10.4927862
941 942 943 944 945 946 947 948 949 950
10.4927862 10.4927862 10.4927862 10.4927862 10.4927862 10.4927862 10.4389072 10.4389072 10.4389072 10.4389072
951 952 953 954 955 956 957 958 959 960
9.6459249 9.6459249 9.6459249 10.6000856 59.4499859 10.6000856 10.6000856 10.6000856 10.2752309 10.2752309
961 962 963 964 965 966 967 968 969 970
10.2752309 10.2752309 10.2752309 10.2752309 60.1683331 10.2752309 11.5972340 12.8954921 10.3481741 10.3481741
971 972 973 974 975 976 977 978 979 980
10.3481741 10.3481741 10.3481741 10.3481741 10.3481741 10.3481741 10.3481741 11.2428492 11.2428492 11.2428492
981 982 983 984 985 986 987 988 989 990
11.2428492 10.0599664 10.0599664 10.0599664 9.3231939 9.3231939 9.3231939 9.3231939 9.7389177 9.7389177
991 992 993 994 995 996 997 998 999 1000
9.7389177 9.3867241 60.6697236 9.3867241 9.3867241 9.3867241 9.3867241 9.2373544 9.2373544 3.5757664
[ reached getOption("max.print") -- omitted 1936 entries ]The take home message from the output is that for every unit increase in the square root of engine displacement there is a -0.14246 decrease in the square root of fuel efficiency (mpg). Therefore, fuel efficiency decreases with increasing engine displacement.
Feauture and coefficients analysis.
coefficients<-coef(ols_ds$finalModel)
coefficients_df <- as.data.frame(coefficients)
features<-rownames(coefficients_df )
coefficients_features <-cbind(coefficients,features)
coefficients_features_df <- as.data.frame(coefficients_features)
feature_analysis<-coefficients_features_df %>%
select(features,coefficients) %>%
filter(!is.na(coefficients)) %>%
arrange(desc(coefficients))feature_analysisols_ds$resultsSee the metrics
actual <- as.vector(testSet$percentage_payment)
actual <- as.data.frame(round(actual,2))
colnames(actual) <- c("actual")
predicted <- as.vector(test_pred)
predicted <-as.data.frame(round(predicted,2))
colnames(predicted) <- c("predicted")library(Metrics)
Attaching package: ‘Metrics’
The following objects are masked from ‘package:caret’:
precision, recall#ape computes the elementwise absolute percent difference between two numeric vectors
absolute_percentage_diff<-ape(actual,predicted)
absolute_percentage_diff <-as.data.frame(round(absolute_percentage_diff,2)*100)
colnames(absolute_percentage_diff) <- c("percentage_difference")actual#Compare actual y values to predicted values
compare_actuals_to_pred<-cbind(actual,predicted,absolute_percentage_diff,testSet)
compare_actuals_to_pred %>%
filter(modality_cost_code_1110==1 & pcn_payer_code_02371001==1) %>%
arrange(desc(percentage_difference))
saveRDS(compare_actuals_to_pred,'DCI_Capstone/data/compare_actuals_to_pred.rds')All features
all_features<-as.data.frame( colnames(compare_actuals_to_pred))
colnames(all_features) = c('features')
all_features #modality cost codes
modality_cost_codes<-all_features %>%
filter(features %like% "modality_cost_code_") %>%
arrange(features)
#diagnosis_codes
diagnosis_codes<-all_features %>%
filter(features %like% "diagnosis_code_") %>%
arrange(features)
#Revenue codes
revenue_codes <-all_features %>%
filter(features %like% "revenue_code_") %>%
arrange(features)
#modifier_1
modifier_1 <-all_features %>%
filter(features %like% "modifier_1_") %>%
arrange(features)
#Modifier_2
modifier_2 <-all_features %>%
filter(features %like% "modifier_2_") %>%
arrange(features)
#Modifier_3
modifier_3 <-all_features %>%
filter(features %like% "modifier_3_") %>%
arrange(features)
#hcpc_code
hcpc_code <-all_features %>%
filter(features %like% "hcpc_code_") %>%
arrange(features)
#pcn_payor_code
pcn_payor_code <-all_features %>%
filter(features %like% "pcn_payer_code_") %>%
arrange(features)compare_actuals_to_pred %>%
filter(pcn_payer_code_02371001 == 1 & modality_cost_code_1110==1) %>%
select(pcn_payer_code_02371001,actual,modality_cost_code_1110,predicted,percentage_difference ) %>%
summarise('avg_actual' = mean(actual),'avg_predicted' = mean(predicted)) %>%
pivot_longer(avg_actual:avg_predicted,names_to = "Prediction_type", values_to = "Percentage_values")pcn_payor_codemodality_cost_codes_shiny#compare_actuals_to_pred %>%
trainSet %>%
filter(pcn_payer_code_02371001 == 1 & modality_cost_code_1110==1,hcpc_code_90999==1 ) trainSet %>%
filter(pcn_payer_code_02371001 == 1 & modality_cost_code_1110==1 &hcpc_code_90999==1 )Bar plots for Actual vs Predicted
compare_actuals_to_pred_plt <-compare_actuals_to_pred %>%
filter(pcn_payer_code_02371001 == 1 & hcpc_code_90999==1 ) %>%
select(actual,predicted,percentage_difference ) %>%
summarise('Actual' = mean(actual),'Predicted' = mean(predicted)) %>%
pivot_longer(Actual:Predicted,names_to = "Predictions", values_to = "percentage_predicted") %>%
ggplot(aes(x = Predictions, y = percentage_predicted,fill = Predictions)) +
geom_text(aes(label=round(percentage_predicted,2)), vjust=0,size=5) +
geom_col( alpha= 0.7) +
scale_fill_manual(values = c("grey","darkslategrey"))+
ggtitle("Comparing Actual Payment % to Predicted % For 02371001 and PNEMOVAX") +
labs(x = "Actual Vs Predicted", y = "Percentage %")+
theme(legend.position = "none")
ggplotly(compare_actuals_to_pred_plt)
NAcompare_actuals_to_pred %>%
filter(pcn_payer_code_02371001 == 1 & hcpc_code_90999==1 ) %>%
select(actual,predicted,percentage_difference ) %>%
pivot_longer(actual:predicted,names_to = "prediction", values_to = "percentage_predicted") %>%
ggplot(aes(x = prediction, y = percentage_predicted,fill = prediction)) +
geom_boxplot(alpha=0.5 ) +
ggtitle("Comparing Actual Payment % to Predicted %") +
labs(x = "Actual Vs Predicted", y = "Percentage")
NALASSO REGRESSION
#Lasso
sum(is.na(algo_df_final))[1] 0library(glmnet)Loading required package: Matrix
Attaching package: ‘Matrix’
The following objects are masked from ‘package:tidyr’:
expand, pack, unpack
Loaded glmnet 3.0-1set.seed(200)
index = createDataPartition(algo_df_final$percentage_payment,p=0.75,list=FALSE)Error in createDataPartition(algo_df_final$percentage_payment, p = 0.75, :
could not find function "createDataPartition"test_pred<- predict(lasso_model,newx=x_testTransformed)
MAE(pred=test_pred,obs=y_test)# 2.091932Error in MAE(pred = test_pred, obs = y_test) :
could not find function "MAE"Compare lasso actual values and predicted values
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 


```{r}
library(tidyverse)
library(ggplot2)
library(plotly)
library(naniar)
library(scales)
library(dplyr)
library(glmnet)
library(caret)
library(viridis)
library(reshape2)
options(scipen = 999)
```

```{r}
#Import data from csv and change datatypes of the columns excluding the columns which are not necessary
raw_data<-read_csv('data/capstone_remit_claims_data.csv')
 
#chnage the columns datatypes to correct data types
df<-raw_data %>%
  mutate(patient_id = as.factor(patient_id),
         location_id = as.factor(location_id),
         #cost_code = as.factor(cost_code),
         modality_cost_code = as.character(modality_cost_code),
         remit_allowed_amount = as.double(remit_allowed_amount),
         contract_number = as.factor(contract_number),
         hct_per = as.double(hct_per),
         epo_units = as.integer(epo_units),
         pcn_payer_code = as.factor(pcn_payer_code),
         type_of_care = as.factor(type_of_care),
         date_of_service = as.Date(date_of_service)
         ) %>% 
  select(-epo_units,-modifier_4,-modifier_5)
 
df<-df %>% 
  mutate('modality' = case_when(modality_cost_code %in% c('1220','1230',"1121") ~ "PD",
                                modality_cost_code %in% c('1110',"1210","1310","1510") ~ "HEMO",
                                modality_cost_code %in% c('1330',"1320") ~ "HOME",
                                TRUE ~ "OTHERS"))

 

df<-df %>% replace_with_na (replace = list(diagnosis_code = 'NULL', urr_modifier = 'NULL',modifier_1 = 'NULL',modifier_2='NULL',modifier_3='NULL'))
df <- df %>% 
      mutate( 'percentage_payment' =  remit_total_paid/remit_total_charged * 100) 
 
#SAVE DATAFRAME
saveRDS(df,'clean_df_for_EDA.RDS')

#payors
payors<-as.data.frame(unique(df$pcn_payer_code))
colnames(payors)<-c("payors")

saveRDS(payors,'DCI_capstone/data/payors.rds')


```
PREPARE A DATAFRAME WHICH CAN BE USED FOR ALGORITHMS
```{r}
#delete unnecessary columns
algo_df<-df %>% 
 select (-date_of_service,-acct_mm,-acct_yyyy,-claim_number,-remit_number,-location_id,-patient_id,-contract_number,-urr_modifier,-description,-percentage_payment)   
 
#dummize categorical columns
dummy_cols <- c("modality_cost_code","diagnosis_code","type_of_care","revenue_code", "modifier_1","modifier_2","modifier_3","revenue_code","hcpc_code","pcn_payer_code")

algo_df<-fastDummies::dummy_columns(algo_df,select_columns = c(dummy_cols),ignore_na = TRUE)
 
#replace na's to 0's
algo_df[is.na(algo_df)]<- 0

 
algo_df_final<-algo_df %>% 
   mutate( 'percentage_payment' =  remit_total_paid/remit_total_charged * 100) %>% 
  select (-modality_cost_code,-cost_code,-diagnosis_code,-type_of_care,-revenue_code,-hcpc_code,-modifier_1,-modifier_2,-modifier_3,-pcn_payer_code,-remit_total_charged,-remit_total_paid,-remits_units,-remit_allowed_amount,-modality)

#SAVE DATAFRAME
saveRDS(algo_df_final,'clean_df_for_algorithm.RDS')
```
```{r}
str(algo_df_final)
```
```{r}
df
```

```{r}
#Final checks
sum(is.na(algo_df_final))
```

SUMMARY ANALYSIS

```{r}
#Charged
summary(df$remit_total_charged)
```
```{r}
#Paid
summary(df$remit_total_paid)
```
 
```{r}
charges_dis_plt<-df %>% 
                  ggplot(aes(x=remit_total_charged, y = ..density..))  + 
                  geom_histogram(breaks = seq(0,2800,by=100 ),bins = 10, binwidth = 0.05,
                  fill =  "chartreuse4", alpha = 0.6) +
                  labs(x="Charges", y = "Counts",title = "Distribution of Charges")+
                  theme_minimal()+
                  geom_density(color = "red", alpha=0.4)+
                  scale_y_continuous(breaks = seq(0,10000,1000))+
                  scale_x_continuous(breaks = seq(0,3000,500))
ggplotly(charges_dis_plt  )

```
```{r}
payment_dis_plt<-df %>% 
              ggplot(aes(x=remit_total_paid,y=..density..))  + 
              geom_histogram(breaks = seq(0,2800,by=100 ),bins = 10, binwidth = 0.05,
                 fill =  "chartreuse4", alpha = 0.6) +
                 labs(x="Payments", y = "Counts",title = "Distribution of Payments")+
                 theme_minimal()+
                 geom_density(color = "red",alpha = 0.4)+
                 scale_y_continuous(breaks = seq(0,10000,1000))+
                scale_x_continuous(breaks = seq(0,3000,500))
ggplotly(payment_dis_plt  )
```


```{r}
pl<- ggplot(df,aes(x=remit_total_charged,y=remit_total_paid) )+
  geom_point(alpha= 0.7)+
  ggplotly(pl)
```

```{r}
 
dci_data_total<-df %>% 
            filter(acct_yyyy == 2017) %>% 
            select('Charges' = remit_total_charged,'Payments'=remit_total_paid)

dci_long <- reshape2::melt(dci_data_total)

#saveRDS(dci_long,'dist_plts.rds')

dist_plts<-ggplot(dci_long, aes(value,y = ..density..)) + facet_wrap(~variable, scales = 'free_x') +
           geom_density(color = "red",alpha = 0.4)+
           labs(title = "Distribution")+
           geom_histogram( fill =  "chartreuse4", alpha = 0.6)+
 # geom_vline(aes(xintercept = mean(value,na.rm=T)),color = "red", linetype = "dashed",size = 1) +
           scale_y_continuous(breaks = seq(0,10000,1000))+
           scale_x_continuous(breaks = seq(0,3000,500))

dist_plts
```


```{r}
df %>% 
  ggplot(aes(x=remit_total_paid)) + geom_histogram(binwidth = 50)
```

 
```{r}
df %>% 
  ggplot(aes(x=remit_total_paid,fill = modality)) + 
    geom_vline(aes(xintercept = mean(remit_total_paid,na.rm=T)),color = "red", linetype = "dashed",size = 1) +
  geom_histogram(bins=40,binwidth = 10, center = 0.05,alpha = 0.8)
```
```{r}
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
dist_box_modality<-df %>% 
                  filter(acct_yyyy == 2017) %>% 
                  select(modality,remit_total_paid) %>% 
                  ggplot(aes(  modality, remit_total_paid,alpha = 0.5)) +
                  geom_boxplot(aes(fill=modality)) +
                  theme_classic() +
                  scale_fill_manual(values = cbp1) +
                  ggtitle("Distribution of Payments across Modalities") +
                  labs(x="Modality",y = "Payments")  +
                    theme(legend.position = "none")

ggplotly(dist_box_modality)
  
```
```{r}
payors
```
```{r}
payors
```
```{r}
df %>% 
                      # filter(pcn_payer_code =='24180402') %>% 
                      #filter(modality == 'PD') %>% 
                       group_by(modality,pcn_payer_code) %>% 
                       summarise(sum_payments= sum(remit_total_paid))
```


```{r}

 
total_payments_bar_plt<-df %>% 
                      # filter(pcn_payer_code =='24180402') %>% 
                      #filter(modality == 'PD') %>% 
                       group_by(modality) %>% 
                       summarise(sum_payments= sum(remit_total_paid)) %>% 
                       ggplot(aes(x = modality, y = sum_payments, fill = modality)) +
                       geom_bar(stat = "identity", alpha=0.8) +
                       theme_classic() +
                       labs(
                          x = "Modality",
                          y = "Total Payments",
                          title = "Total Payments by Modality"
                          ) +
                      scale_fill_manual(values = cbp1) +
                      theme(legend.position = "none")

    ggplotly(total_payments_bar_plt)
```


END OF SUMMARY ANALYSIS

BEGIN TIME SERIS ANALYSIS
 
```{r}
 avg_pct_paid_over_time<-df %>% 
  filter(acct_yyyy == 2018) %>% 
  mutate('percentage_paid' = remit_total_paid/remit_total_charged*100) %>% 
  mutate('period' =  paste(acct_mm , '/' , acct_yyyy)) %>% 
  select(acct_mm,acct_yyyy,period,percentage_paid,pcn_payer_code) %>% 
  group_by(acct_mm,period,pcn_payer_code) %>% 
  summarise('avg_pct_paid'=mean(percentage_paid))  

time_plot<-ggplot(avg_pct_paid_over_time, aes( reorder(period,acct_mm) , avg_pct_paid, group = pcn_payer_code, color = pcn_payer_code)) + 
  geom_line(size = 1) +
  geom_point(size=2, shape=6, aes(fill=factor(pcn_payer_code))) +
  theme_minimal()+
  labs(color = "Payors", fill = 'Payors')+
  ylab('Percentage of Payments ') +
  xlab('Month/Year') +
  ylim(0, 50)+
  ggtitle('Percentage of Payments by Payor over time')+ 
  theme(axis.text.x = element_text(angle = 90,hjust=1))

ggplotly(time_plot)
```

```{r}
 df %>% 
  filter(acct_yyyy == 2018) %>% 
  mutate('percentage_paid' = remit_total_paid/remit_total_charged*100) %>% 
  mutate('period' =  paste(acct_mm , '/' , acct_yyyy)) %>% 
  select(acct_mm,acct_yyyy,period,percentage_paid,pcn_payer_code) %>% 
  group_by(acct_mm,acct_yyyy,period,pcn_payer_code) %>% 
  summarise('avg_pct_paid'=mean(percentage_paid)) %>% 
  arrange(acct_mm,avg_pct_paid) %>% 
  ggplot(aes( reorder(x = period,acct_mm) ,y = avg_pct_paid,group=1,color=pcn_payer_code,fill=pcn_payer_code)) + 
  geom_point() +
    theme(axis.text.x = element_text(angle = 45,hjust=1))

```
 
 ANALYSIS BY LOCATION (can be a page)
```{r}
# payment percentage by location
df %>% 
  group_by(location_id) %>% 
  summarise('Num_of_rows'=n(),
            'Total_charges' = sum(remit_total_charged),
            'Total_payments' = sum(remit_total_paid),
            'percentage_payments' = (Total_payments/Total_charges) * 100,
            ) %>% 
  arrange(desc(Total_charges))

```
ALANYSIS  BY PCN (PAYORS)
```{r}

# payment percentage by PCN
charge_payments<- df %>% 
                  group_by(pcn_payer_code) %>% 
                  summarise('Num_of_rows'=n(),
                   'Total_charges' = sum(remit_total_charged),
                    'Total_payments' = sum(remit_total_paid),
                     'percentage_payments' = (Total_payments/Total_charges) * 100,
                      ) %>% 
                  arrange(percentage_payments)
```
```{r}
charge_payments %>% 
  mutate('mean_percentage_payments' = sum(Total_payments)/sum(Total_charges)*100) %>% 
  mutate('dis_from_avg_per_payment' =  percentage_payments-mean_percentage_payments )
```
```{r}
payors_with_good_per<-df %>% 
                      group_by(pcn_payer_code) %>% 
                      summarise('Num_of_rows'=n(),
                                'Total_charges' = sum(remit_total_charged),
                                'Total_payments' = sum(remit_total_paid),
                                'percentage_payments' = (Total_payments/Total_charges) * 100,
                                ) %>% 
                      arrange(desc(Total_charges)) %>% 
                      ggplot(aes(reorder(x=pcn_payer_code,percentage_payments),y=percentage_payments,fill = pcn_payer_code)) +
                      geom_col(alpha=0.8) +
                      theme_minimal()+
                      theme(axis.text.x = element_text(angle = 45,hjust=1))+
                      labs(x='Payors',y="Percentage of charges paid",title = "Which payors are paying good percentage?")

ggplotly(payors_with_good_per)

```
#Which payer is paying good percentage by modality
```{r}
df %>% group_by(pcn_payer_code,modality) %>% 
                      summarise('Num_of_rows'=n(),
                                'Total_charges' = sum(remit_total_charged),
                                'Total_payments' = sum(remit_total_paid),
                                'percentage_payments' = (Total_payments/Total_charges) * 100,
                                ) %>% 
                      arrange(desc(Total_charges))
```

```{r}
 payors_with_good_per<-df %>% group_by(pcn_payer_code,modality) %>% 
  filter(pcn_payer_code=='02371001') %>% 
                      summarise('Num_of_rows'=n(),
                                'Total_charges' = sum(remit_total_charged),
                                'Total_payments' = sum(remit_total_paid),
                                'percentage_payments' = round((Total_payments/Total_charges) * 100,2)
                                ) %>% 
                      arrange(desc(Total_charges)) %>% 
                      ggplot(aes(reorder(x=modality,percentage_payments),y=percentage_payments,fill = modality)) +
                      geom_col(alpha=0.8) +
                      geom_text(aes(label=percentage_payments), vjust=0) + 
                      theme_minimal()+
                      labs(x='Modality',y="Percentage of charges paid",title = "Percentage of Charges paid by Modality?")+
                      theme( theme(legend.position = "none"))

ggplotly(payors_with_good_per)
```
```{r}
df  

  
```

 
```{r}
 payors_with_good_per<-df %>% group_by(pcn_payer_code,modality) %>% 
  filter(pcn_payer_code=='02371001') %>% 
                      summarise('Num_of_rows'=n(),
                                'Total_charges' = sum(remit_total_charged),
                                'Total_payments' = sum(remit_total_paid),
                                'percentage_payments' = round((Total_payments/Total_charges) * 100,2)
                                ) %>% 
                      arrange(desc(Total_charges)) %>% 
                      ggplot(aes(reorder(x=modality,percentage_payments),y=percentage_payments,fill = modality)) +
                      geom_segment(aes (reorder(x=modality,percentage_payments), xend =modality,y=0,yend = percentage_payments) )+
                      geom_point(color = "orange",size = 4)+
                      theme_minimal()+
    geom_text(aes(label=percentage_payments)) +  
                      labs(x='Modality',y="Percentage of Charges Paid(%)",title = "Percentage of Charges Paid by Modality?")+
                      scale_y_continuous(breaks = seq(0,20,1))+
                      theme(legend.position = "none")
  

ggplotly(payors_with_good_per)
```
 


BY LOCAITON, WHICH PAYORS ARE CONTRIBUTING MORE (CAN BE A TAB)
```{r}
df %>% 
  group_by(location_id,pcn_payer_code) %>% 
  summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
  mutate('loss_amt' = total_charges-total_paid) %>% 
  mutate('payment_percentage' = (total_paid/total_charges)*100) %>% 
  mutate('loss_percentage' = (loss_amt/total_charges) * 100) %>% 
  filter(total_paid!=0) %>% 
  arrange(location_id,desc(payment_percentage))
```

```{r}
unique(df$pcn_payer_code)
```
```{r}
colnames(df)
```

```{r}
distinct(df,acct_yyyy,pcn_payer_code ) %>% 
  filter(acct_yyyy==2018) 
```

```{r}
#unique(df$pcn_payer_code)
df%>% 
                group_by(description) %>% 
                filter (pcn_payer_code =='00410235') %>% 
               summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
                mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>% 
                mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) 
                #filter(total_paid>0)
```

Services by each payor type
```{r}
services_plt <- df%>% 
                group_by(description) %>% 
                filter (pcn_payer_code =='02371001') %>% 
               summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
                mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>% 
                mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>% 
                filter(total_paid>0) %>% 
                ggplot(aes(reorder(x= description,total_paid),y = total_paid,fill = description)) +
                geom_col()+
                coord_flip()+
                theme(axis.text.x = element_text(angle = 45,hjust=1)) +
                theme_minimal()+
                xlab("Services") +
                ylab("Payments") +
                ggtitle("Total Payments by Services")+
                theme(legend.position = "none") 

ggplotly(services_plt)
```


FOR WHAT TYPE OF TREATMENT / MEDICATION ARE WE GETTING PAID LESS / MORE THAN 10 PERCENT (CAN BE A PAGE)
```{r}
x<- df %>% 
      group_by('Description'=description) %>% 
      summarise('total_charges'=sum(remit_total_charged),
      'total_paid' = sum(remit_total_paid)) %>% 
      mutate('percentage_paid' = round(total_paid/total_charges * 100,2)) %>% 
      arrange(desc(percentage_paid)  )

saveRDS(x$Description,'data/meds.rds')
```
```{r}
df %>% 
  filter(description == 'ARANESP/DARBEPOETIN ALFA-' ) %>%   
  group_by(description) %>% 
  summarise('TotalCharges'=sum(remit_total_charged),'TotalPaid' = sum(remit_total_paid)) %>% 
 pivot_longer(TotalCharges:TotalPaid,names_to = "Type", values_to = "Charges")
```

```{r}
#what percentage are we paid back overall for a description
 df %>% 
      group_by('Description'=description) %>% 
      summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
      mutate('percentage' = round(total_paid/total_charges * 100,2)) %>% 
      select (Description,percentage) 
```


Medications for which less than 10 percent paid
```{r}
##top cost codes
meds<-df %>% 
  filter(description == 'ARANESP/DARBEPOETIN ALFA-' ) %>%   
  group_by(description) %>% 
  summarise('TotalCharges'=sum(remit_total_charged),'TotalPaid' = sum(remit_total_paid)) %>% 
 pivot_longer(TotalCharges:TotalPaid,names_to = "Type", values_to = "Charges")%>%
  ggplot(aes(x=reorder(description,Charges),y=Charges,fill = Type) ) +
  geom_bar(stat="identity",alpha = 0.8,position = 'dodge') +
  theme(axis.text.x = element_text(angle = 45,hjust=1)) +
  theme_minimal()+
  xlab("Medication") +
  ylab("Dollars") +
  ggtitle("Total Charges vs Total Paid") 

ggplotly(meds)
```
 
  
  



 
```{r}
meds%>% 
      arrange(desc(avg_per_paid))
```
 
Difference in payments by modality on medications
```{r}
#only medications paid by cost_code
meds<- df %>% 
      select(modality_cost_code,cost_code,description,remit_total_charged,remit_total_paid,modality) %>% 
      filter(cost_code>3000 ) %>%  
      mutate('percentage_paid' = round(remit_total_paid/remit_total_charged * 100,2)) %>% 
     group_by(modality, description) %>% 
    summarize('avg_per_paid'=mean(percentage_paid))  

meds<- meds%>% 
      filter(avg_per_paid <54)

options(repr.plot.width = 3, repr.plot.height = 3)

 x<- ggplot(meds,aes(reorder(x=description,avg_per_paid), y = avg_per_paid,fill =  modality )) +
  geom_col(stat="identity",position = 'dodge' ) +
  ggtitle("Medication Payments by Modality") + 
  labs(x="Medications", y = 'Average of percentage Payments') +
  theme(axis.text.x = element_text(angle = 45,hjust=1)) 
 
 ggplotly(x)
```

```{r}
df %>% 
  select(modality_cost_code,cost_code,description,remit_total_charged,remit_total_paid) %>% 
    filter(cost_code>3000 & cost_code<5000) %>%  
    mutate('percentage_paid' = round(remit_total_paid/remit_total_charged * 100,2)) %>% 
    group_by(modality_cost_code,cost_code,description) %>% 
  summarize('avg_per_paid'=mean(percentage_paid)) %>% 
  arrange(cost_code,modality_cost_code) 
```
List of HCPC codes for drugs
```{r}
distinct(df,cost_code,description,modality_cost_codes) 

 

```


ANALYSIS ON TREATMENTS
```{r}
##only treatments
treatments<-df %>% 
            group_by(cost_code) %>% 
            filter(cost_code<1510) 

treatment_summaries<-treatments %>% 
  mutate('treatment_type' = case_when(modality_cost_code %in% c('1110','1210','1310') ~ "1110",
                                        TRUE ~ modality_cost_code)) %>% 
  group_by(treatment_type,description) %>% 
  summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
  mutate('percentage_paid' = round(total_paid/total_charges * 100,2)) %>% 
  arrange(desc(percentage_paid))

treatment_summaries
  
```

 
```{r}
treatment_summaries %>% 
ggplot(aes(reorder(x=description,total_paid),y=total_paid,fill = description))+
  geom_bar(stat="identity")+
  coord_flip()+
  theme_bw()+
    theme(axis.text.x = element_text(angle = 45,hjust=1))+
  # geom_text(aes(label=total_paid), vjust=0) + 
  xlab("Treatment Types") +
  ylab("Number of Treatments") +
  ggtitle("Payments by Treatments") +
  theme(legend.position = "none")
  
```
```{r}
treatment_summaries %>% 
ggplot(aes(reorder(x=description,percentage_paid),y=percentage_paid,fill = description))+
  geom_bar(stat="identity")+
  geom_text(aes(label=percentage_paid), vjust=0) + 
  #coord_flip()+
    theme(axis.text.x = element_text(angle = 45,hjust=1))+
  xlab("Treatments") +
  ylab("Percentage of Charges paid back") +
  ggtitle("For which Treatments Good Percentage of Charge is Paid Back")+
    theme(legend.position = "none")
```
```{r}


ggdotchart(treatment_summaries, x = "description", y = "percentage_paid",
           color = "description",                                # Color by groups
           palette = c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7"),
           # Custom color palette
           sorting = "ascending",                        # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           ggtheme = theme_pubr(),
           size = 8
           ) +
   geom_text(aes(label=percentage_paid), vjust=0,size=3) + 
theme(legend.position = "none") + 
  theme(axis.text.x = element_text(angle = 360,hjust=1))+
  coord_flip() +
  labs(title = "Percentage Paid Back by Treatment Types",x="Treatment Types", y = "Percentage %")
```
 
PAYMENTS STRUCTURE OF PAYORS
 
```{r}
df %>% 
  group_by(pcn_payer_code) %>% 
 summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
  mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>% 
    mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2))
```

```{r}
df %>% 
  group_by(pcn_payer_code) %>% 
    filter (pcn_payer_code !='02371001') %>% 
 summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
  mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>% 
    mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>% 
arrange(desc(per_payment_overall))  %>% 
  ggplot(aes(reorder(x=pcn_payer_code,per_payment_overall),y=per_payment_overall,fill = reorder(x=pcn_payer_code,per_payment_overall))) +
  geom_col(stat="identity") +
    theme(axis.text.x = element_text(angle = 90,hjust=1)) +
    theme(legend.position = "none")+
    ggtitle("Other Payors payment percentage")+
    labs(x= "Payors",y="Percentage of Payments")
    theme(legend.position = "none")+
      theme_minimal()
  
  
```
 
```{r}
bp<-df %>% 
  group_by(pcn_payer_code) %>% 
  filter (pcn_payer_code !='02371001') %>% 
 summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
  mutate('percentage_of_payments' = round(total_paid/total_charges*100,2)) %>% 
    mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>% 
  ggplot(aes(x="",y=percentage_of_payments,fill = pcn_payer_code))+
  geom_bar(width = 1, stat = "identity")

 pie_chart <- bp + coord_polar("y",start = 0) +
   labs(title = "Payment percentage by payor") +
   theme_void()  
pie_chart
 
 
```
```{r}
test <- compare_actuals_to_pred %>% 
                                # filter(!!as.symbol(input$algo_payors) == 1 ) %>% 
                                # filter(!!as.symbol(input$algo_modality) == 1)%>% 
                                filter(pcn_payer_code_02371001 == 1 & hcpc_code_90999==1 ) %>% 
                                select(actual,predicted,percentage_difference ) %>%
                                summarise('Actual' = mean(actual),'Predicted' = mean(predicted)) %>% 
                                pivot_longer(Actual:Predicted,names_to = "Predictions", values_to = "percentage_predicted")

get_percentage_value<-test  %>% 
  filter(Predictions == "Predicted") %>% 
  select(percentage_predicted)

 
get_percentage_value 
                            
```

 

```{r}
Payors_bar_plt<-df %>% 
                  group_by(pcn_payer_code) %>% 
                  summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
                  mutate('percentage_of_payments' = round(total_paid/sum(total_paid)*100,2)) %>% 
                  ggplot(aes(reorder(x=pcn_payer_code,percentage_of_payments),y=percentage_of_payments,fill = pcn_payer_code,text=percentage_of_payments))+
                  theme(axis.text.x = element_text(angle = 90,hjust=1)) +
                  geom_bar(  stat = "identity")+
                  theme(legend.position = "none") +
                  labs(x="Payors",y="Percentage of Total  Payments",Title = "Percentage of Total Payments For Each Payor ")

ggplotly(Payors_bar_plt,tooltip = "text")
```


PERCENTAGE OF PAYMENTS BY PAYOR
```{r}
bp<-df %>% 
  group_by(pcn_payer_code) %>% 
 summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
  mutate('percentage_of_payments' = round(total_paid/sum(total_paid)*100,2)) %>% 
  ggplot(aes(x="",y=percentage_of_payments,fill = pcn_payer_code))+
  geom_bar(width = 1, stat = "identity")
bp

 pie_chart <- bp + coord_polar("y",start = 0) +
   labs(title = "Majority of Payments coming from Payor: 02371010") +
   theme_void()  

pie_chart
#92.58
 
```
TAKE OUT THE MAJOR PAYOR AND SEE HOW OTHERS ARE DOING
```{r}

bp<-df %>% 
  group_by(pcn_payer_code) %>% 
 summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
  mutate('percentage_of_payments' = round(total_paid/sum(total_paid)*100,2)) %>% 
    mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>% 
  filter(pcn_payer_code !='02371001') %>% 
  ggplot(aes(x="",y=percentage_of_payments,fill = pcn_payer_code))+
  geom_bar(width = 1, stat = "identity")
bp

 pie_chart <- bp + coord_polar("y",start = 0) +
   labs(title = "Payments By Other Payors") +
   theme_void() 
 
   geom_text(aes(y = value/3 + c(0, cumsum(value)[-length(value)]), 
            label = percent(value/100)), size=5)

pie_chart
```

 
 
 
```{r}
ggplot(df) + 
geom_bar(aes(x=pcn_payer_code, y=remit_total_paid, color=pcn_payer_code),stat="identity") + geom_smooth(aes(x=pcn_payer_code, y=remit_total_paid))+
  theme(axis.text.x = element_text(angle = 90,hjust=1)) +
    theme(legend.position = "none")
```
 

```{r}
df %>% 
  filter(pcn_payer_code!='02371001') %>% 
  select (pcn_payer_code,modality_cost_code,remit_total_paid) %>% 
  group_by(modality_cost_code,pcn_payer_code) %>% 
  summarise('avg_paid' = mean(remit_total_paid)) %>% 
  ggplot() + geom_col(aes(x=pcn_payer_code, y=avg_paid,fill = pcn_payer_code))+
   theme(axis.text.x = element_text(angle = 90,hjust=2)) +
    theme(legend.position = "none")
```

 
PAYMENTS BY TREATMENTS
```{r}
ts<-treatment_summaries %>% 
 ggplot(aes(x=reorder(description,percentage_paid),y=percentage_paid,fill = description))+
  geom_bar(stat = "identity")+
    theme(axis.text.x = element_text(angle = 45,hjust=1)) +
    theme(legend.position = "none")
ts

 
```
PAYMENTS BY NON-TREATMENT SERVICES COST CODES like meds supplies etc
```{r}

df %>% 
  filter(cost_code >'1510') %>% 
  group_by(cost_code ) %>% 
summarise('total_charges'=sum(remit_total_charged),'total_paid' = sum(remit_total_paid)) %>% 
  mutate('percentage_paid' = total_paid/total_charges * 100) %>% 
  filter(percentage_paid>0) %>% 
  mutate('per_payment_overall' = round((total_paid/sum(total_paid))*100,2)) %>% 
  arrange(desc(per_payment_overall)) %>% 
  ggplot(aes(x= factor(cost_code),y=per_payment_overall,fill = factor(cost_code))) +
  geom_col(stat_count = "identity")+
   theme(axis.text.x = element_text(angle = 90,hjust=2)) +
    theme(legend.position = "none")
```
```{r}
 
  distinct(df,cost_code,description)%>% 
  filter(cost_code %in% c('3605','3621','7060','3613'))
```
 


```{r}
df %>% 
  select(date_of_service,patient_id,cost_code,remit_total_paid,pcn_payer_code) %>% 
  ggplot(aes(x=as.factor(cost_code),y=remit_total_paid)) +geom_point()+
  theme(axis.text.x = element_text(angle = 90,hjust=1)) +
  labs(x="Cost Codes",y="Total payments",title = "Total Payments by Cost Codes")
```
IS THERE ANY RELATION BETWEEN PAYMENT AND HCT_PER? NO
```{r}
pl <- ggplot(df, aes(x=hct_per,y=remit_total_paid) ) +
  geom_point(alpha=0.2) + geom_smooth(method = 'lm') +
  scale_x_log10() +
  scale_y_log10()+
  labs(x="HCT values",y="Total charges",title = "Correlation between charges and HCT values")

pl
 
```
```{r}
colnames(df)
```




ANALYSIS USING MODIFIERS

G1 - Most recent URR of less than 60%
G2 - Most recent URR of 60% to 64.9%
G3 - Most recent URR of 65% to 69.9%
G4 - Most recent URR of 70% to 74.9%
G5 - Most recent URR of 75% or greater
G6 - ESRD patient for whom less than seven dialysis sessions have been provided in a month.
```{r}

#PERCENTAGE PAID BY pcn's without modifiers

no_modifiers<-df %>% filter(is.na(modifier_1)  & is.na(modifier_2) & is.na(modifier_3))
 

no_modifiers<-no_modifiers %>% 
  group_by(pcn_payer_code) %>% 
  summarise('total_charges' = sum(remit_total_charged),'total_payment'=sum(remit_total_paid)) %>% 
mutate ('pct_paid_with_no_modifiers'=total_payment/total_charges*100)
 
 
with_modifiers<-df %>% filter(!is.na(modifier_1 ) | !is.na( modifier_2)   | !is.na( modifier_3) )
#percentage paid by pcn's with modifiers
with_modifiers<-with_modifiers %>% 
  group_by(pcn_payer_code) %>% 
  summarise('tc' = sum(remit_total_charged),'tp'=sum(remit_total_paid)) %>% 
mutate ('pct_paid_with_modifiers'=tp/tc*100)
 
 
#merge  
modifiers<- merge(with_modifiers,no_modifiers,by = "pcn_payer_code") %>% 
  select(pcn_payer_code,pct_paid_with_modifiers,pct_paid_with_no_modifiers) %>% 
  pivot_longer(
    pct_paid_with_modifiers:pct_paid_with_no_modifiers,
    names_to = "with_or_without_modifiers",
    values_to = "percentage_paid")
  
 modifiers_plt<-ggplot(modifiers,aes(y = percentage_paid, x = pcn_payer_code,fill = with_or_without_modifiers)) +
  geom_bar(stat= "identity", position = 'dodge') +
  labs(x="Payors", y = "Payments %") +
  ggtitle("Difference in Payments With and Without Modifiers")+
    scale_y_continuous(breaks = seq(0,20,1))
  
  
 ggplotly(modifiers_plt)

```

DIAGNOSIS CODE ANALYSIS
 
```{r}
dplyr::count(df,diagnosis_code,sort = TRUE)
```

N25.81 - Secondary hyperparathyroidism of renal origin
Z23 is a billable ICD code used to specify a diagnosis of encounter for immunization.

```{r}
diagnosis<-df %>% group_by(diagnosis_code) %>% summarise(n=n(),total_charged = sum(remit_total_charged), total_paid = sum(remit_total_paid)) %>% arrange(desc(n))

diagnosis

```
 
```{r}

diagnosis %>% 
  filter(!is.na(diagnosis_code)) %>% 
  ggplot(aes(x=diagnosis_code,y=n,fill=diagnosis_code)) +
  geom_col() +
    theme(axis.text.x = element_text(angle = 90,hjust=1)) 
```

 
```{r}
corrs
```
```{r}
 head(algo_df_final)
```


BEGIN MAKING PERCENTAGE OF PAYMENTS PREDICTION USING REGRESSION ALGORITHMS.

1. ORDINARY LEAST SQUARES MODEL
```{r}
#First, split the data in algo_df_final dataset with 75 percentile
set.seed(237)
index = createDataPartition(algo_df_final$hct_per,p=0.75,list = FALSE)

#save the 75% of data in trainSet and 25% in test set.
trainSet <- algo_df_final[index,]
testSet <- algo_df_final[-index,]
```
```{r}
#OLS
#train the model 
#ols_ds <- train(percentage_payment~.,data = trainSet,method = "lm", metric ="RMSE", 
 #              trControl = trainControl(method = "none"))

ols_ds <- train(percentage_payment~.,data = trainSet,method = "lm")

#training set performance
train_pred <- predict(ols_ds, newdata = trainSet)
MAE(pred = train_pred , obs = trainSet$percentage_payment)#1.555435

```
```{r}
print(ols_ds)
```


```{r}
#test set
test_pred <- predict (ols_ds, newdata = testSet)
MAE(pred = test_pred, obs = testSet$percentage_payment) 
```
```{r}
print(test_pred)
```

```{r}
#put predictions in dataframe
test_pred.df<- as.data.frame(as.vector(test_pred))
```
```{r}
ols_ds
```
 

The take home message from the output is that for every unit increase in the square root of engine displacement there is a -0.14246 decrease in the square root of fuel efficiency (mpg). Therefore, fuel efficiency decreases with increasing engine displacement.

Feauture and coefficients analysis.
```{r}
coefficients<-coef(ols_ds$finalModel)
coefficients_df <- as.data.frame(coefficients)

features<-rownames(coefficients_df )
coefficients_features <-cbind(coefficients,features)
coefficients_features_df <- as.data.frame(coefficients_features)
 
feature_analysis<-coefficients_features_df %>% 
  select(features,coefficients) %>% 
  filter(!is.na(coefficients)) %>% 
  arrange(desc(coefficients))
```

```{r}
feature_analysis
```
 
 

```{r}
ols_ds$results
```


See the metrics
```{r}
actual <- as.vector(testSet$percentage_payment)
actual <- as.data.frame(round(actual,2))
colnames(actual) <- c("actual")
 
predicted <- as.vector(test_pred)
predicted <-as.data.frame(round(predicted,2))
colnames(predicted) <- c("predicted")
```
```{r}
library(Metrics)
#ape computes the elementwise absolute percent difference between two numeric vectors
absolute_percentage_diff<-ape(actual,predicted)
absolute_percentage_diff <-as.data.frame(round(absolute_percentage_diff,2)*100)
colnames(absolute_percentage_diff) <- c("percentage_difference")
```
```{r}
actual
```

```{r}
#Compare actual y values to predicted values
compare_actuals_to_pred<-cbind(actual,predicted,absolute_percentage_diff,testSet)
compare_actuals_to_pred %>% 
  filter(modality_cost_code_1110==1 & pcn_payer_code_02371001==1) %>% 
  arrange(desc(percentage_difference))

saveRDS(compare_actuals_to_pred,'DCI_Capstone/data/compare_actuals_to_pred.rds')
```

```{r}
#compare_actuals_to_pred <- cbind(testSet$percentage_payment,test_pred.df,testSet)
#colnames(compare_actuals_to_pred ) <- c("actual_per_values","predicted_per_values")
```
All features
```{r}
all_features<-as.data.frame( colnames(compare_actuals_to_pred))
colnames(all_features) = c('features')

all_features 
```

```{r}
#modality cost codes
modality_cost_codes<-all_features %>% 
                      filter(features %like% "modality_cost_code_") %>% 
                      arrange(features)
#diagnosis_codes
diagnosis_codes<-all_features %>% 
                filter(features %like% "diagnosis_code_") %>% 
                arrange(features)
#Revenue codes
revenue_codes <-all_features %>% 
                filter(features %like% "revenue_code_") %>% 
                arrange(features)
#modifier_1
modifier_1 <-all_features %>% 
                filter(features %like% "modifier_1_") %>% 
                arrange(features)
 
#Modifier_2
modifier_2 <-all_features %>% 
                filter(features %like% "modifier_2_") %>% 
                arrange(features)
 
#Modifier_3
 modifier_3 <-all_features %>% 
                filter(features %like% "modifier_3_") %>% 
                arrange(features)

#hcpc_code
hcpc_code <-all_features %>% 
                filter(features %like% "hcpc_code_") %>% 
                arrange(features)
#pcn_payor_code 
pcn_payor_code <-all_features %>% 
                filter(features %like% "pcn_payer_code_") %>% 
                arrange(features)
```
```{r}
compare_actuals_to_pred %>% 
  filter(pcn_payer_code_02371001 == 1 & modality_cost_code_1110==1) %>% 
  select(pcn_payer_code_02371001,actual,modality_cost_code_1110,predicted,percentage_difference ) %>%
  summarise('avg_actual' = mean(actual),'avg_predicted' = mean(predicted)) %>% 
  pivot_longer(avg_actual:avg_predicted,names_to = "Prediction_type", values_to = "Percentage_values")
```
```{r}
pcn_payor_code
```
```{r}
modality_cost_codes_shiny
```

```{r}
#compare_actuals_to_pred %>% 
trainSet %>% 
  filter(pcn_payer_code_02371001 == 1 & modality_cost_code_1110==1,hcpc_code_90999==1  ) 
```
```{r}
trainSet %>% 
  filter(pcn_payer_code_02371001 == 1 & modality_cost_code_1110==1 &hcpc_code_90999==1 )
```


Bar plots for Actual vs Predicted
```{r}

compare_actuals_to_pred_plt <-compare_actuals_to_pred %>% 
                              filter(pcn_payer_code_02371001 == 1 & hcpc_code_90999==1 ) %>% 
                              select(actual,predicted,percentage_difference ) %>%
                              summarise('Actual' = mean(actual),'Predicted' = mean(predicted)) %>% 
                              pivot_longer(Actual:Predicted,names_to = "Predictions", values_to = "percentage_predicted") %>% 
                              ggplot(aes(x = Predictions, y = percentage_predicted,fill = Predictions)) +
                              geom_text(aes(label=round(percentage_predicted,2)), vjust=0,size=5) + 
                              geom_col( alpha= 0.7) +
                              scale_fill_manual(values = c("grey","darkslategrey"))+
                              ggtitle("Comparing Actual Payment % to Predicted % For 02371001 and PNEMOVAX") +
                              labs(x = "Actual Vs Predicted", y = "Percentage %")+
                              theme(legend.position = "none")

ggplotly(compare_actuals_to_pred_plt)
  
```

 
```{r}
compare_actuals_to_pred %>% 
  filter(pcn_payer_code_02371001 == 1  & hcpc_code_90999==1 ) %>% 
   select(actual,predicted,percentage_difference ) %>% 
 pivot_longer(actual:predicted,names_to = "prediction", values_to = "percentage_predicted") %>% 
  ggplot(aes(x = prediction, y = percentage_predicted,fill = prediction)) +
  geom_boxplot(alpha=0.5 ) +
  ggtitle("Comparing Actual Payment % to Predicted %") +
  labs(x = "Actual Vs Predicted", y = "Percentage")
  
```

 


LASSO REGRESSION
```{r}
#Lasso
sum(is.na(algo_df_final))
```
 

```{r}
library(glmnet)
set.seed(200)
index = createDataPartition(algo_df_final$percentage_payment,p=0.75,list=FALSE)

trainSet <- algo_df_final[index,]
testSet <- algo_df_final[-index,]
 
x_train <- trainSet %>% select(-percentage_payment) %>%  as.matrix()
x_test <- testSet %>% select(-percentage_payment) %>% as.matrix()

y_train <- trainSet$percentage_payment
y_test <- testSet$percentage_payment
 
#lets standardize the data
preProcValues <- preProcess(x_train, method = c("center","scale"))

x_trainTransformed <- predict(preProcValues,x_train)
x_testTransformed<- predict(preProcValues,x_test)

#train the model
lasso_model <- glmnet(x_trainTransformed,y_train,alpha = 1,lambda = NULL)

train_pred<- predict(lasso_model,newx=x_trainTransformed)
MAE(pred = train_pred, obs = y_train) 
```
```{r}
test_pred<- predict(lasso_model,newx=x_testTransformed)
MAE(pred=test_pred,obs=y_test)# 2.091932

#use crossfold
cv <- cv.glmnet(x_trainTransformed,y_train, alpha=1)#alpha = 1 means lasso and 0 means ridge
 
cv$lambda.min

#retrain the model with minimum lamdba value
fit=glmnet(x_trainTransformed,y_train, alpha = 1, lambda = cv$lambda.min)

test_predictions_refitted<- predict(fit,newx=x_testTransformed)

MAE(pred=test_predictions_refitted,obs=y_test)#1.596442
```
Compare lasso actual values and predicted values
```{r}
y_test.df <- as.data.frame(as.vector(y_test))
y_test.df
```
```{r}
test_predictions_refitted <- as.data.frame(as.vector(test_predictions_refitted))
test_predictions_refitted

final_results<- cbind(y_test.df,test_predictions_refitted,x_test)
 
final_results

#see the residuals for each payor and treatment codes.
```

```{r}
head(x_trainTransformed)
```

```{r}
plot(lasso_model)
```

```{r}
 rownames(coef(fit,s=0.1))
```
```{r}
coef(fit,s=0.1)
```

```{r}
print(fit)
```
```{r}
plot(lasso_model,xvar="lambda", label = TRUE)
```
```{r}
cvfit = cv.glmnet(x_trainTransformed,y_train)
```
```{r}
plot(cvfit)
#https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html
```

```{r}
cvfit$lambda.min
```
```{r}
coef(cvfit, s = "lambda.min")
```



```{r}
library(coefplot)
coefplot(lasso_model,sort='magnitude')
```
```{r}
typeof(trainSet)
```
```{r}
predictors <- colnames(trainSet)
```
 


 

